Data-driven modeling in biology and medicine (Part 2)

Kang-Ling Liao

University of Manitoba

"Mathematical Modeling of Breast Cancer Treatment with Radiation, Anti-estrogen, and Immune Checkpoint Inhibitor"

Radiotherapy (RT) and endocrine therapy (ET) are standard treatments for estrogen receptor-positive (ER+) breast cancer, but they could induce resistance and relapse issues. Immune checkpoint inhibitor (ICI) is another potential treatment for breast cancer, but its response rate is low. In this work, we create a system of ordinary differential equations to investigate the combination treatments among RT, ET, and ICI in ER+ breast cancer. Our model quantitatively captures the tumor growth data under the combination among these three treatments for different ER+ breast cancer cell lines. Our numerical predictions indicate that: (i) potential treatment to reduce the relapse caused by RT; (ii) potential breast cell lines have a better response rate to anti-PD-1; (iii) Tumor elimination and no relapse could appear in the combination of RT and ET in MCF-7 ER+ tumor cells; (iv) these treatments have induce better tumor reduction is which breast cancer cell lines. We also study the distribution of parameter values calibrating to different ER+ breast cancer cell lines to categorize (virtual) cohort patients and to provide potential biomarkers for selecting appropriate treatment for patients

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Tracy Stepien

University of Florida

"Modeling Tumor-Immune Interactions in the Glioblastoma Microenvironment"

Glioblastoma (GBM) is an aggressive brain tumor that is extremely fatal with no current treatment options available that can achieve remission. One potential explanation for minimally effective treatments is due to the characteristically high immune-suppressive glioma microenvironment. We develop an agent-based model to simulate the interactions of glioma cells, T cells, and myeloid-derived suppressor cells (MDSCs) and the effects of oxygen, a T cell chemoattractant, and an MDSC chemoattractant. To validate our model and quantify cell clustering patterns in GBM, we use spatial statistics comparing simulations to data extracted from cross-sectional tumor images of cellular biomarkers.

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Harsh Jain

University of Minnesota Duluth

"Looking Beyond Data: Simulating Treatment Outcomes for Unobserved Heterogeneous Populations Using Preclinical Insights"

Developing new cancer drugs involves significant investments of time and resources, yet many promising candidates fail during clinical trials. One potential reason for this failure is that preclinical testing typically relies on genetically identical animals and uniform cell lines, which do not reflect the diversity found in actual patient populations. Additionally, preclinical data is often presented in aggregated form, masking important individual-level differences that could inform clinical predictions. In this talk, I will present a case study of non-small cell lung cancer xenograft treatment with radiation to introduce our Standing Variations Modeling approach, which addresses these issues in two main steps. First, we deconstruct aggregated preclinical data – specifically, average tumor volume time-courses and Kaplan-Meier survival curves – to recover individual-level variation, uncovering hidden differences among study subjects (“who’s in”). Second, we use these insights to simulate treatment outcomes for broader, more diverse virtual populations through computational modeling (“who’s out”). A key innovation in our method is the assignment of a personalized survival probability to each virtual participant, explicitly linked to their unique disease dynamics. This mechanistic connection allows us to capture inter-individual variability and supports meaningful extrapolation to unobserved populations. By moving beyond aggregate data and homogeneous preclinical models, this approach offers a more nuanced and practical path to clinical translation.

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Negar Mohammadnejad

University of Alberta

"Strategies for Optimizing the Efficacy of Oncolytic Virus–Immune System Interactions"

Oncolytic virotherapy (OVT) is an innovative cancer treatment in which oncolytic viruses are introduced into a patient to selectively target and destroy tumor cells. In the absence of these viruses, tumors are known to create an immunosuppressive environment. However, upon administration of oncolytic viruses and initiation of virotherapy, the immune system is activated, leading to a robust anti-tumor response. Despite this, oncolytic viruses alone have rarely been shown to induce complete and sustained regression of established tumors in vivo. In this talk, I will discuss key strategies for enhancing the efficacy of oncolytic virotherapy. These include the integration of immunotherapy approaches with virotherapy to amplify anti-tumor immune responses, as well as optimizing the timing, dosage, and sequencing of viral administrations to maximize therapeutic benefits. By refining these strategies, we aim to improve treatment outcomes and potentially enhance the therapeutic impact of oncolytic virotherapy.

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